KR20100126431A - Automatically prescribing total budget for marketing and sales resources and allocation across spending categories - Google Patents

Abstract

In one embodiment, in a manner intended to optimize sales results, such as revenue for the target sale, based on empirically obtained quantitative economic data, (1) the overall budget for marketing and sales resources for the target sale and (2) A software facility (“facilities”) that utilizes the qualitative description of the object offering is provided to automatically specify both allocations of the overall budget across a plurality of spending categories (also referred to as 'activities').

Description

AUTOMATICALLY PRESCRIBING TOTAL BUDGET FOR MARKETING AND SALES RESOURCES AND ALLOCATION ACROSS SPENDING CATEGORIES}

This application is directed to: 1) US Provisional Application No. 61 / 030,550, filed Feb. 21, 2008, 2) US Provisional Application No. 61 / 084,252, filed July 28, 2008, 3) July 2008. U.S. Provisional Application No. 61 / 084,255, filed May 4, U.S. Provisional Application No. 61 / 085,819, filed August 1, 2008, and 5) U.S. Provisional Application No. 61, filed August 1, 2008 / 085,820, which are incorporated herein by reference.

The described technique relates to the field of automated decision support tools, specifically to the field of automated budgeting tools.

Marketing communication ("marketing") is a process that allows sellers of a product or service-that is, "offering" to educate potential buyers about the property. Marketing is often the major cost of sellers and often consists of a number of components or categories, such as various different advertising media and / or outlets, and other marketing techniques. Despite the complexity involved in developing marketing budgets attributing levels of spending to each of a number of components, there are few useful automated decision support tools, so it is common practice to perform this activity manually based on subjective decisions. And in many cases adverse consequences.

In some cases where useful decision support tools exist, it is common for a user of the tool to provide a large amount of data and past results of their assignment of marketing resources to a subject offering. In many cases, such as in the case of a new sale, such data is not available. Even when such data is available, it can be inconvenient to access this data and provide it to the decision support tool.

Thus, tools and their various components that automatically specify the allocation of funds or other resources to the property in favor of the user without having to provide historical performance data for the property will be of considerable utility.

1 is a high level data flow diagram illustrating the flow of data within a typical arrangement of components used to provide facility.
2 is a block diagram illustrating some of the components typically included in at least some of the computer systems and other devices on which the facility is executed.
3 is a table diagram illustrating sample content of a library of past marketing efforts.
4 is a display diagram showing a sign-in page used by the facility to limit access to the facility to authorized users.
Fig. 5 is a flow chart showing page display generated by the facility in the view / edit mode.
6-9 illustrate the displays provided by the facility to request information about the item for sale for the overall marketing budget and its allocation to be specified by the facility.
FIG. 10 is a display diagram illustrating a results navigation display provided by the facility after collecting information about the subject property to allow the user to select a form of analysis for reviewing the results.
FIG. 11 is a display diagram illustrating that a display provided by a facility is determined for a subject for sale to convey an optimal overall marketing budget of the facility.
12 is a display provided by the facility to show expenditure mix information. This display includes the overall budget 1201 specified by the facility.
13 is a process diagram illustrating collecting additional property information from a user.
FIG. 14 is a process diagram illustrating the development of three measures: cognition, affect, and experience derived for a subject offering.
FIG. 15 is a table diagram illustrating sets of respective marketing activity assignments for different combinations of the three derived attributes shown in FIG. 14.
16 is a process diagram showing how the initial allocation specified by the table of FIG. 15 should be adjusted for a number of special conditions.
FIG. 17 is a process diagram illustrating how the facility determines a dollar amount for expenditure in each marketing activity.
FIG. 18 is a process diagram showing final adjustments to the results shown in FIG. 17. FIG.
FIG. 19 is a display showing a display provided by the facility to represent resource allocation prescriptions made by the facility with respect to a number of related subject offerings, such as the same product packaged in three different forms. Degree.
20-23 are display diagrams illustrating a typical user interface presented by the facility in some embodiments for specifying and automatically collecting data inputs.
24-26 illustrate screenshots for a facility that provides a method of digital purchase for any resource or media channel.

The following description is intended to describe various embodiments of the invention. Accordingly, the specific modifications discussed should not be construed as limitations on the scope of the invention. Those skilled in the art will clearly appreciate that various equivalents, changes, and modifications may be made without departing from the scope of the present invention, and that such equivalent embodiments should be included herein.

(1) the overall budget for marketing and sales resources for the target item and (2) by optimizing sales results, such as revenue for the target item, based on empirically obtained econometric data. A software facility (“facilities”) that utilizes the qualitative description of the object for sale is automatically provided to automatically specify all allocations of the overall budget for a plurality of spending categories (also called “activities”).

In the initialization phase, the facility considers data about past marketing efforts for various items that do not have a relationship necessary for the marketing effort for the target item. This data includes, for each such effort, (1) the characteristics of the marketed property; (2) the overall marketing budget; (3) allocation among marketing activities; And (4) reflect business results. This data can be obtained in a variety of ways, such as by direct marketing studies or by acquisitions from academic publications.

The facility uses this data to create resources suitable for the purposes of the facility. First, the facility calculates an average elasticity measure for the overall marketing budget across all past marketing efforts, which predicts the impact on sales results of allocating specific levels of resources to the overall marketing budget. Second, the facility derives a number of adjustment factors that, relative to the average elasticity measure for the overall marketing budget, specify how the average elasticity measure for the overall marketing budget increases or decreases to reflect certain characteristics of past marketing efforts. . Third, for each past marketing efforts of multiple groups of qualitatively similar items, the facility measures per-elasticity elasticity measures that indicate the extent to which each marketing activity influenced the sales outcomes of the marketing efforts for the group. Per-activity elasticity measures are derived.

The facility uses interviewing techniques to request a qualitative description of the subject offering from the user. The facility uses some of the requested qualitative descriptions to identify adjustment factors to apply to the average elasticity measure for the overall marketing budget. The facility is the average resiliency of the overall marketing budget adjusted by the identified adjustment factors to generate the highest level of revenue for the target property or to identify the ideal overall marketing budget that is expected to maximize the other objectives specified by the user. Use a version of the scale.

After identifying the ideal overall marketing budget, the facility uses the requested qualitative description of the target offering to determine which of the groups of other listings most closely matches the elasticity per activity derived for that group. A set of ideal marketing activity assignments are derived from a set of measures.

In some embodiments, the facility may include syndicated media, distributed sales data, internet media, internet behavioral data, natural search query data, paid search activity data. media data such as search activity data, television, radio, print, consumer behavior data, tracking survey data, economic data, weather data, financial data such as stock market, competitive marketing spend data data), and data received from one or more of a number of types of external sources, including online and offline sales data.

In some embodiments, the facility uses resource elasticities to combine work-amended resource allocations created using two different optimization schemes based on different user inputs. ) Or use a uniform set of lift factors. In some embodiments, the facilities provide functionality for purchasing and scheduling marketing resources according to allocations recommended by the facility. In some embodiments, the facility optimizes resource allocations within multimedia type and / or multiplatform media providers.

In this way, the facility automatically assigns the overall marketing resource allocation and allocation to the target item without the user having to provide historical performance data for the target item.

The sales or market response curve determined by the facility predicts sales results as a mathematical function of various resource drivers.

Sales = F (any set of driver variables)

Here, F represents a statistical function with appropriate economic characteristics of diminishing returns.

In addition, since this relationship is based on data that is time series, cross-section, or both time series and cross-section, the method inherently has direct, indirect and interactive effects on underlying conditions. Yields them.

These effects describe how sales respond to changes in basic driver variables and data structures. These response effects are mainly known as "lift factors". As a special subset or case, these methods allow reading any on-off condition for cross sections or time series.

There are various classes of statistical functions suitable for determining and applying different types of lift factors. In some embodiments, the facility uses a class known as multiplicative and log log (using natural log) and point estimates of lift factors.

In certain situations, the facility uses methods that apply to categorical driver data and to categorical results. These include classes of stochastic lift factors known as multinomial logit, logit, probit, non-parameterized or hazard methods.

In various embodiments, the facility uses a variety of different types of lift factors determined in various ways. References herein to "elasticity" in many cases extend to various other types of lift factors.

1 is a high level data flow diagram illustrating the data flow within a typical arrangement of components used to provide facility. A number of web client computer systems 110 under user control generate page view requests 131 and send them to the logical web server 100 via a network such as the Internet 120. These requests typically include various types of other requests related to receiving information about page view requests and target offerings and providing information about the designated overall marketing budget and its distribution. Within a web server, all of these requests may be routed to a single web server computer system or may be load-balanced among multiple web server computer systems. The web server typically responds to each with a provided page 132.

While various embodiments are described with respect to the environment described above, those skilled in the art will appreciate that a facility may be implemented in a variety of other environments including a single, monolithic computer system, as well as various other combinations of computer systems or similar devices connected in various ways. I will understand. In various embodiments, various computing systems or other different client devices may be used in place of web client computer systems such as mobile phones, PDAs, televisions, cameras, and the like.

2 is a block diagram illustrating some of the components typically included in at least some of the computer systems and other devices on which the facility is executed. The computer systems and apparatus 200 may include one or more CPUs 201 for executing computer programs; Computer memory 202 for storing programs and data during use; Permanent storage device 203, such as a hard drive, for permanently storing programs and data; A computer readable medium drive 204 such as a CD-ROM drive for reading data and programs stored on the computer readable medium; And a network connection 205 for connecting the computer system to other computer systems, such as via the Internet. Typically, computer systems configured as described above are used to support the operation of the facility, although those skilled in the art will appreciate that the facility may be implemented using various types and configurations of devices, including various components. .

3 is a table diagram illustrating sample content of a library of past marketing efforts. Library 300 consists of entries such as entries 310, 320, 330, etc., each entry corresponding to a set of one or more past marketing efforts, each of which shares a similar context. Each entry includes values for new product attribute 311, cognitive score attribute 312, sentiment score attribute 313, experience score 314, message clarity score 315, and message persuasive score 316. , A plurality of context attribute values valid for past marketing efforts corresponding to the entry. Each entry contains the following statistical measures of past marketing efforts corresponding to the entry: log of outcome (351), base (352), log of results with lag factor. values for outcome with a lag factor (353), log of external (354), log of relative price (355), and log of relative distribution (356). It includes more. Each entry is a TV 361, print 362, radio 363, outdoor 364, internet search 365, internet query 366, Hispanic advertising efficiency values for each of a number of categories, including hispanic, 367), direct, 368, events, 369, sponsorship 370, and others 371. ) Logs further.

4 is a display diagram illustrating a login page used by the facility to limit access to the facility to authorized users. The user enters his e-mail address in field 401 and his password in field 402 and selects login control 403. If the user is having difficulty logging in this way, the user selects control 411. If the user does not already have an account, the user selects control 421 to create a new account.

FIG. 5 is a flow diagram illustrating page display generated by the facility in view / edit mode. The display lists a number of scenarios 501-506, each corresponding to an existing offering prescription created for the user or for the organization with which the user is associated. For each scenario, the display includes the name of the scenario (511), the description of the scenario (512), the date the scenario was created (date, 513), and the status of the scenario. The user can select any of the scenarios, such as by selecting its name, or its state, to obtain more information about the scenario. The display also includes a tap area 550 that the user can use to navigate different modes of facility. In addition to tabs 552 for the current view / edit mode, the tab area includes tabs 551 for create mode, tabs 553 for compare mode, and tabs for send mode. 554, and a tab 555 for delete mode. The user can select any of these tabs to activate the corresponding mode.

6-9 illustrate the displays provided by the facility to request information about the target property for which the overall marketing budget and its allocation will be specified by the facility. Figure 6 shows current revenue (601), current annual marketing spending (602), anticipated growth rate (603) across industries, gross revenue expressed as a percentage of revenue. and controls for entering values for attributes such as profit, 604, and market share, 605, expressed as a percentage of the dollar. The display is a save control 698 selectable by the user to store the attribute values entered by the user, and a continue control selectable by the user to proceed to the next display for entry of context attribute values. 699 more.

7 is another display provided by the facility to request attribute values for the subject property. This includes controls for context attributes, inputs for industry newness 701, market novelty 702, channel novelty 703, and marketing innovation 704.

8 is another display provided by the facility to request attribute values. This provides controls that the user can use to enter values for contextual attributes: novelty 801 of marketing information content, company position 802 in market, market share 803, and pricing strategy 804. Equipped.

9 is another display provided by the facility to request attribute values. This includes a control 901 that the user can use to determine whether customer segment detail will be included. The display further includes charts 910 and 920 for specifying values of additional context attributes. The chart 910 may be used to simultaneously specify values for clarity and consistency of branding messaging and positioning efforts by the company responsible for the subject offering. have. To use the chart 910, the user selects a single cell in the grid included in the chart that corresponds to appropriate values of both consistency and clarity attributes. Similarly, part 920 allows the user to simultaneously select appropriate values for the persuasiveness and likeability of the company's advertisements.

FIG. 10 is a display diagram illustrating a result navigation display provided by the facility after collecting information about the subject property to allow the user to select a form of analysis for the review results. The display includes a control 1001 that the user can select to review market share information related to the results, a control 1002 that the user can select to review the spending mix information related to the results, and a revenue and A control 1003 may be selected to review the loss information.

FIG. 11 is a display diagram illustrating a display provided by the facility to convey an optimal overall marketing budget determined by the facility for the object. The display shows two curves: revenue for the entire marketing budget (or "marketing spend"), 1120, and a profit for the overall marketing budget (ie, "marketing contribution after cost"). Includes a graph 1110 showing 1130. The facility identified point 1131 as being the peak of the revenue curve 1130 and thus identified $ 100, which is the level of the corresponding marketing spend, as the optimal marketing spend. The height of point 1131 represents the predicted level of revenue to be generated by marketing spending, and the height of point 1121 represents the predicted level of overall revenue predicted from this marketing spending. Table 1150 provides additional information on optimal marketing spending and its calculations. The table includes, for each of the current marketing expenditures 1161, the ideal marketing expenditures 1162, and the delta 1163 between the two, revenues planned for this level of marketing expenditures 1151; Costs of goods and services 1152 predicted to be incurred at this level of marketing expenditure; Gross margin 1153 to be earned at this level of marketing spending; Marketing spend 1154; And marketing contribution after cost 1155 predicted at this level of marketing expenditure.

To define the profit curve and identify the overall marketing budget level at which it reaches its peak, the facility first determines the overall marketing budget elasticity that is appropriate for the target offering. This elasticity value is in the range between 0.01 and 0.30 and is overridden to remain within this range. The facility calculates elasticity by adjusting an initial elasticity value, such as 0.10 or 0.11, in accordance with a number of adjustment factors, each associated with a particular attribute value for the property. Sample values for these adjustment factors are shown in Table 1 below.

Industrial novelty Marketing innovation New information Market share Ad quality height 0.05 0.1 0.05 -0.03 0.04 middle 0 0 0 0 0 lowness -0.02 -0.03 -0.02 0.02 -0.03

The industrial novelty column corresponds to the control 701 shown in FIG. 7. For example, if the topmost checkbox of the control 701 is checked, the facility selects an adjustment factor of 0.05 from the industry novelty column; If either of the two boxes in the middle of control 701 is checked, the facility selects coordinator 0 from the industry novelty column; If the bottommost checkbox of control 701 is checked, the facility selects an adjustment factor of -0.02 from the industry novelty column. Similarly, the marketing innovation column corresponds to the control 704 shown in FIG. 7, the new information column corresponds to the control 801 shown in FIG. 8, and the market share column corresponds to the control 803 shown in FIG. 8. Corresponds to. The Ad Quality column corresponds to the charts 910 and 920 shown in FIG. In particular, the sum of the positions of the cells selected in the two graphs with respect to the lower left corner of each graph is used to determine the high, medium, or low level of advertisement quality.

The facility then uses the adjusted overall marketing budget elasticity to determine the level of the overall marketing budget at which the maximum return occurs, as described in Table 2 below.

12 is a display provided by the facility to show spending mix information. The display includes the overall budget 1201 specified by the facility. The user can edit this budget if he wants to see the impact on the distribution information shown below. The display also includes controls 1202 and 1203 that the user can use to identify particular issues regarding the designation of the marketing budget. The display further includes a table 1210 showing various information for each of the plurality of marketing activities. Each row 1211-1222 identifies a different marketing activity. Each row contains the following columns: current percentage allocation 1204, ideal percentage allocation 1205, dollar allocation to brand in thousands, 1206, dollar for product in thousands It is further divided into allocations (dollar allocation to product in thousands, 1207), and dollar difference in thousands between current and ideal. For example, from row 1214, the facility specifies reducing the allocation for print advertising from 15% to 10%, of which $ 3.3 million will be spent on print advertising for the brand, $ 2.2 million Will be spent on print advertising for the product, and the current allocation for print marketing is $ 1.85 million more than the ideal allocation. The display further includes a portion 1230 that the user can use to customize the bar chart report to include or exclude any of budget and marketing activities. It can be seen that the user selects check boxes 1231-1233 to cause portions 1250, 1260, and 1270 to be added to a report that includes bar graphs for TV, radio, and print marketing activities. . The portion 1250 for TV marketing activity includes a bar 1252 for assigning a current percentage to nationwide TV, a bar for assigning current percentage to cable TV (1253), and a bar for ideal percentage allocation to nationwide TV (1257). , And a bar 1258 for ideal percentage allocation for cable TV. The other report parts are similar.

13-18 illustrate a process for the facility to determine the activity distribution shown in FIG. 12. 13 is a process diagram illustrating the collection of additional property information from a user. In some embodiments, this additional attribute information is obtained from the user using a user interface similar in design to that shown in FIGS. 6-9. FIG. 13 illustrates a number of attributes 1300 for which values are requested from a user for a target offering.

FIG. 14 is a process diagram illustrating the derivation of three derived measures, ie cognition, affect, and experience, for a subject offering. The values for these derived measures are derived based on the values of the attributes shown in FIG. 13 provided by the user for the object for sale.

FIG. 15 is a table diagram illustrating sets of respective marketing activity assignments for different combinations of the three derived attributes shown in FIG. 14. For example, FIG. 15 shows TV 44%, Print Magazine 12%, Print Newspaper 0%, Radio 5%, Outdoor 0%, Internet Search 10%, Internet for target items that are assigned high recognition scores and moderate sentiment. Marketing resources should be allocated in percentages of 5% advertising, 12% direct marketing, 7% sponsorships / events, 5% PR / other, and 0% distance. Each of these nine groups of assignments is based on relative activity elasticities, such as those shown in FIG. 3, grouped by cognitive and sentiment scores represented for groups of past marketing efforts included in the library. .

FIG. 16 illustrates how the initial allocation specified by the table of FIG. 15 should be adjusted for a number of special conditions 1600.

17 is a process diagram showing how the facility determines the dollar amount spent on each marketing activity. The process 1700 determines the size of the target audience specified by the user and divides it by the emotional percentage of the target to purchase purchased reach-i.e., the number of users to whom the marketing message is to be provided. Acquire it. This number is multiplied by the adjusted percentage of allocation to obtain frequency per customer, which is then multiplied by a number of purchase cycles per year and cost per impression. The estimated expenditure for each activity is obtained.

FIG. 18 is a process diagram showing the final adjustment to the results shown in FIG. 17. Process 1800 specifies scaling up or down the target viewer to meet the overall marketing budget determined by the facility for the subject sale.

FIG. 19 is a display diagram illustrating a display provided by a facility to represent resource allocation assignments by the facility with respect to the number of related object offerings, such as the same product packaged in three different forms. The display includes a chart 1910 that graphically represents each of the relevant object offerings, pack A, pack B, and pack C, each of which has a circle. The location of the center of the circle represents the current and ideal overall marketing budget allocated to the property, such that the distance and direction of each circle from the 45 ° line 1920 indicates how much marketing expenditure should be increased or decreased for the property. For example, the fact that the circle 1911 for Pack A is in the upper left of the 45 ° line indicates that for Pack A marketing spending should be increased. In addition, the diameter and / or area of each circle reflects the overall revenue contribution to the item, assuming that the ideal overall marketing budget specified by the facility for the corresponding item of sale is adopted. The display also includes a portion 1930 that includes a bar graph representing the current and ideal market share and volume for each related target item. The display also includes a portion 1940 that shows information similar to that shown in portion 1150 of FIG. 11.

In some embodiments, the facility may include syndicated media, distributed sales data, internet media, internet behavioral data, natural search query data, paid search activity data. media data such as search activity data, television, radio, print, consumer behavior data, tracking survey data, economic data, weather data, financial data such as stock market, competitive marketing spend data data), and data received from one or more of a number of types of external sources, including online and offline sales data.

In various embodiments, the facility includes one or more of the following additional aspects discussed in more detail below.

1) Minimum Distance Matching of communication touchpoints to brand / client needs

2) Classification methodology for communication needs (cognition, emotion and experience)

3) interactions between traditional and internet media, and experience factors;

4) Joint optimization of core media, internet media and experience factors

5) Combinations of user-specific multi-source data (USMSD) on the results and driver variables required for calculation

6) Intelligent Automation of Data Stacks for Modeling

7) Intelligent automation of model specifications, statistical estimation and expertise

8) Use dynamic, real-time Internet "native" search data as predictive momentum (DNM) indicators of marketing and brand response.

9) Measure dynamic interactions, optimizations, predictions and projections of results using marketing drivers, brand momentum and marketing ROI.

10) Reporting Brand / Client Results

1) minimum distance matching

(1.1) The facility uses these three dimensions and low, medium, and high (numerically 1, 2, 3) using input questions about information (Qx), affect (Qy), and experience (Qz). Coded) to classify brand / client communication needs.

(1.2) The facility may allocate resources through any of a very large number of communication touchpoints, also known as communication channels. For each channel, Facilit takes into account the ability of the "medium" to convey the information, emotion and experience dimensions of brand / client communications.

In selecting the communication channels, the facility minimizes the “distance” between the communication request and the medium / channel, thereby selecting the touchpoints involved in the market response, and subsequent application of elasticity and ideal economic calculations.

Distance is defined as the sum of squared differences (SSD) between brand / client needs and media / channel.

^ Stands for exponentiation.

2) Classification method

The classification method is described in sections 1.1 and 1.2 above.

3) Interaction between traditional and internet media

The core outcome equation is defined (where else):

Additional resources are multiplied by the right hand side.

The facility combines the traditional medium in (3) as a so-called "direct path" that links resources and results.

The facility extends this model to include the Internet in two ways.

     Method 3.1 is to add and include Internet metrics for online display and paid search with traditional media (TV, print, radio, etc.).

     Method 3.2 is to add and include one or more variables / metrics (VINS) for Internet "natural" searches. An example of natural search is count data for words used in internet search boxes (as distinguished from impressions and clicks).

The facility then adds and applies a second "indirect path" equation, whereby Internet natural search is described by traditional marketing and sales resources.

These two equations work "recursively".

In fact, marketing and sales resources drive the attention and discovery of consumers / markets. Discovery behavior is measured by natural search. Subsequently, subsequent to the recursive process, Internet resources “convert” attention to action.

4) Joint Optimization

The direct and indirect path equations then provide the dynamics for the "topline" of economics optimization.

The facility applies various resource input levels, flows the results through the regressive topline equations to produce the results, and then applies the associated elasticity (for yield) and the margin and cost of the associated resources.

Furthermore, in some cases, the facility extends this method to a third equation, whereby paid search is also treated to be comparable to natural search. Thus, paid search is an intermediate result.

Any dynamic, momentum, intermediate or interim brand metric (awareness, consideration, buzz) is handled using this third mathematical method.

5) USMSD ( User - Specific Multi - Source Data )

Demand / result equations

● brand specific;

External industry specific;

Data for marketing and sales resources;

● Internet-specific data related to brands / users / clients

Requires data inputs.

The facility is unique in bringing these four data streams together for the purpose of modeling demand using the two-mathematical method outlined above.

5.1) Typically, brand data includes volumetric sales, pricing, revenue, new customer counts, existing customer counts, customer retention, customer attrition, and customer upsell of products or services ( upsell / crosssell. This also includes industry and brand / client attributes from input questions.

5.2) External data includes a series of external arguments and drivers. Typically, they include elements describing economic conditions and trends, weather, competitor marketing and sales resources, and the like.

5.3) Marketing and sales data includes various measures of resource entry. These may include resources consuming for communication media / touchpoints. These include physical measures of resources for media / touchpoints (physical units such as direct mail counts or time-based, rating points).

5.4) Internet specific data mainly includes a measure of natural search using word counts and counts of word clusters and semantic phrases. Typically, these word measures are more general or generalized to the brand name itself, aspects of key phrasing associated with the brand (so-called universal selling proposition), aspects of brand positioning such as quality and brand. The words that were written.

20-23 are display diagrams illustrating a typical user interface presented by the facility in some embodiments to designate and automatically collect some or all of these data inputs. 20 is an initial display that includes a list of sales categories from which the user selects the most suitable category.

FIG. 21 shows a dashboard showing the data retrieval status for four categories of data inputs 2110, 2120, 2130 and 2140. Each type has status indicators 2111-2113 for status indicators, e.g., Internet data category 2110, to indicate the search status of data of that category. In addition, the user can click on any of the data types to view detailed information about that type of data.

22 shows a detailed display of the data in the marketing and sales data category. This display 2200 includes a number of different components 2211 in the marketing and sales data categories, status indicators 2212 indicating the search status of each of the components, and controls that can be manipulated by the user to initiate a search of each component. Show them (2213).

23 shows a display. The display includes controls 2311 for inputting natural and paid search terms related to the property; Controls 2312 for specifying relevant periods for each natural search and paid search; And controls 2313 for specifying where frequency data for natural and paid searches are to be retrieved and stored.

6) intelligent data stack

The facility uses the data dashboard user interface shown in Figs. 20-23 to allow users to select a suitable set of results and driver data and the financial factors to be used by the facility.

The facility then provides a data entry template for each data class (see 5.1, 5.2, 5.3, and 5.4 above).

The facility then applies a set of quality and data scrubbing algorithms to the user to verify the overall integrity, consistency and accuracy of the designated data streams.

The facility then converts and loads these data vectors into a matrix (MOM) for the modeling of the overall facility.

The row structure for the MOM typically includes time dimensions, customer segments, channels of trading, and / or geographic layers.

The thermal structure for the MOM typically includes final result variables, intermediate result variables and driver variables (see 5.1, 5.2, 5.3 and 5.4).

The facility uses so-called log / log transformations for data and demand model specifications.

The facility applies a generalized least squares (GLS) method for statistical estimation of various equations.

The facility also constitutes any necessary "dummy" variables used in metrology economics, including seasonality.

7) intelligent estimation

The facility includes linkage and comparison methods across GLS estimates for Candidate Models (CM), statistical diagnoses, t values and model / mathematical coefficients.

The facility performs GLS estimation of about 40 CM variants and associated diagnostics (the facility includes numerical algorithms and methods for GLS).

The facility then selects and uses BLUS (best, linear, unbiased estimates) of the response coefficients (response elasticities) for economic optimization of resource levels and mix.

This choice is consistent with the best fit, the best t-values, the absence of multi-collinearity, the absence of serial correlation, the appropriate numerical sign (positive and negative), and the Expert Library. CEL) is determined by elasticity estimation.

8) DNM ( Dynamic Native Momentum )

As described above, word counts and word count clusters related to and derived from Internet natural search include and deal with concepts for brand momentum, brand quality and brand image.

The facility categorizes these word / semantic concepts into driver variables related to and used in the 2-mathematical direct path and indirect path equations (see above). These semantic "buckets" can include counts of received queries related to the brand name itself, counts related to product or service categories and brand / client competitors, and more generalized topics (eg hybrid technology vehicle vs. lexus). RXH).

The facility includes dynamic feeds of word counts from natural search from wireless and mobile devices, and search providers such as Google, Yahoo or MSN or others (MySpaces, Facebook, YouTube).

DNM data is typically a dynamic sample of ongoing Internet traffic. The facility uses counts per "x" million queries.

9) optimize the dynamic use of the Internet in the momentum of the expected (prediction) and prediction (forecasting)

The facility uses a two-mathematical method outlined above to configure top-down optimization of brand / client goals related to resource drivers. Drivers here include both traditional marketing and sales, and pricing and internet resources.

The facility uses both direct calculation (closed form calculus) and branch and bound (B & B) heuristic methods to calculate ideal results using the domain of resource drivers.

10) Facility reporting on brand / client results and conclusions

The facility includes visual reporting and GUIs for brand / client results (see Compass SMB, Compass Agency and Compass USMSD / DNM here). For example, in various embodiments, the facility displays results using one or more of a sales response curve, a revenue curve, and a current vs. ideal bar graph.

In various embodiments, the facility allocates resources across some or all of these channels, and in some cases, additional channels as follows.

television

Movie Theaters

radio

newspaper

magazine

prints

Customer magazine

Loose inserts

Internet advertising

Internet search

Brand / Company Website

e-mail

outdoors

Home Shopping TV

Product placement

airport

public transport

Sponsorship of sporting events

Sponsorship of other events

hospital

800 / free call

Mail at home

Celebrity endorsement

In-store advertising

In-store investigation

Promotions and special offers

Product sample

Recommended by relatives and family

Expert Recommendation

Video on demand

Video games

Streaming video

Interactive TV

Specification text table

" ACE "Coordinated multisource market  Response Resiliency Library

Market response optimization (MRO) typically includes (1) best, linear, unbiased estimates (BLUS) of resource response resiliency parameters based on data that implements (1) appropriate variations in resource levels and mixes, and (2) appropriate data observations. Need.

In some embodiments, the facility uses a four-step method to calculate the BLUS estimate of elasticity using cross-brand and cross-resource third party data. The four-step method uses ACE-L metadata in combination with consistent third party data about results and drivers, further combined with the best statistical methods for BLUS.

Values and results are a comprehensive database of crossbrand crossmedia resilience used for resource optimization. This holistic methodology uses the ACE-L score to (1) the net effect of resources spent on sales results across a wide range of cross-brand and cross-resource conditions, and (2) alternatives to define "content impacts". Allow and measure impacts of conventional methods.

Multi-source data

There are two main data classes for modeling: results and drivers. For econometric modeling, the ACE method typically uses combined time-series and cross-section data.

For Multi-Source Library (MSL) and results (dependent variables), ACE uses a consistent definition of sales revenue for brands / services in the library.

For MSL and resource drivers, the ACE uses a range of independent variables.

Step 1: The facility obtains data for these drivers from third party data providers. For example, data series for media consumed by period, market location and media type may be obtained from one or more third party sources. Data classes include economy, competition, tracking, pricing, channel funds, salesforce, retail store conditions, offline marketing and online marketing, And predetermined momentum data.

Typically, such 3PDS (3 ^rd Party data sources) is a client - and to know the differences in the specific transaction data (see below Errors in variable) or understanding. However, these differences are generally considered to be consistent.

The cross sections in MSL consist of brand / service, geography and more. We apply 3PDS resource drivers that are defined consistently within, or across, the library data for brands and the like. In fact, the facility eliminates data fluctuations due to differences in data definitions across brands / clients.

ACE  Tuned Dynamic Parameters

The basic method is to define the Sales = Base Volume times (Marketing Resource) ^ Elasticity parameter, where ^ represents the natural index.

For each brand (ie data record), the facility defines its ACE score on the 1-5 scale for emotion (A), cognition (C), and experience (E). The facility also adds one factor (L) for local market or time sensitivity.

Step 2: The facility then extends modeling using the following specification.

Each record (section) in the library uses and contains an ACE-L score.

Thus, the media type's capacity for delivering content related to emotion, perception and experience, and upward and downward movement in elasticity due to brand characteristics are allowed.

For example, increasing the emotional score needed to motivate the consumer as a result allows for increased elasticity of the TV medium for other brands with different content goals in this situation. Lift factors for print and the Internet increase with information needs. Lifts for outdoors, radio and newspapers increase with local market focus.

Complete of response resiliency BLUS  calculation

Basic or core elasticity parameters without ACE-L use the following formula:

Core equation:

Each resource similarly extends this formula. Other factors driving "Delta", including innovation, are described in Compass®.

Step 3: The facility forwards the ACE adjustment to this core equation in order to replace Delta. The result is a series of direct effects and "interactions" with ACE components as additional drivers. For example:

Proper estimation of these direct and interactive parameters requires that data and equations be consistent with certain rules.

One rule or assumption is that error terms are distributed independently and equally despite similar variances.

However, due to the cross-sectional design, some aspects of the homogeneity hypothesis will not be satisfied.

This condition is known as heteroskedasticity.

Step 4: To correct the heteroscedasticity, the facility applies both Generalized Least Square (GLS) estimation and corresponding "weights" using fixed effects on the cross sections.

Other rules include correcting for serial correlation using lag terms.

Additional functionality

In some embodiments, the facility is a uniform set of resource elasticity or lift factors to combine work-amended resource allocations created using two different optimization schemes based on different user inputs. Use In some embodiments, the facilities provide functionality to purchase and schedule marketing resources according to allocations recommended by the facility. In some embodiments, the facility optimizes resource allocations within multimedia type and / or multiplatform media providers.

(1) Hybrid Anchoring for distance and output parameters (Hybrid Anchoring )

In some embodiments, two main methods (Mix1 and Mix2) are available to the facility to determine the optimal resource mix for the media types and communication channels.

Mix1 applies fully computational calculus in that it optimizes client goals (eg, volume or revenue) that depend on those constraints if they are in the constraints. Numerical methods include sales revenue or revenue goal functions and calculus to find maximums. The facility solves the set of derivatives for the ideal resource level by type by taking the first derivative for each driving resource (media type). The end result is that the ideal resource level and mix depend on both the elasticities by the media type and the cost of the resources (as measured in dollars). Upon completing these calculations, the ideal resource mix is equivalent to the ratio of each elasticity. These elasticities as applied by the facility are obtained from the library and applied to the scenario profile of the user.

Since media channels and touchpoints are evolving rapidly, the facility also includes a second method for computing an ideal mix, which is performed using ACE (Ffect, Cognition, Experience) attributes. Here, the brand “position” is defined by the user's scenario profile and specific question (and scale) for emotional, cognitive and experience attributes.

For ACE Mix2, the library includes and applies ACE scales for each media channel and touchpoint. For Mix2, the facility prohibits media types that do not apply, selects media types by minimizing the distance to the brand ACE position for communications, and impressions to “layer” the media types into the mix in an ideal manner. Each calculation applies a reach, ideal frequency and cost.

In some embodiments, one of the Mixl method and the Mix2 method may be used alone, or the two may be used in combination because one method or another method may be more applicable to the desired user or media channels. In many situations, there may or may be an overlap in information with the available media channels. For example, there is typically overlap for internet channels (display, paid search) or print or television or the like.

If its calculations have an "overlap", the facility combines the two methods depending on the fact that the elasticities in Mix1 provide a causal link to the result (volume, revenue).

Given Mix2 and overlapped resource OR1, the facility centers the calculation using the known Mix1 elasticity (KME1) and calculates each of the remaining elasticities as a ratio. An example is as follows.

(2) method of digital purchase for any resource or media channel

Referring to the screenshot of Figure 24, which calculates the ideal budget and mix for the user's goals, the facility also provides functionality that allows the user to purchase and schedule or "flight" each resource or media type. Include. Each media purchase may be scheduled monthly, selecting all months of the year, or any particular subset of months. The recommended amount may be equally distributed or changed according to the purchaser's desire. This is illustrated by the screenshot of FIG.

In the screenshot of FIG. 25, this facility represents its recommended total resource allocation (“Total Planned Spend”). Vertically stacked horizontal bands correspond to different media types (eg, television, radio, printing, internet browsing, internet display, etc.). For each media type, the facility uses the user interface to determine the recommended resource allocation for that media type (e.g., $ 17,748 for television), and the amount the user has committed to that media type (currently Displays $ 0) for each of the media types. To request the purchase of a particular type of media for each upcoming month, or a "flight" in which the media will be purchased, the user selects the checkbox corresponding to the month and assigns a dollar value assignment below that month. Enter it. These entered values are reflected in the "requested spend" indication for each medium type.

In some embodiments (not shown), the horizontal band for each media type is assigned to the media provider for that media type, such as physical location, time of day, day of week, or various other targeting information, information specifying or identifying a creative, and the like. Contains additional information useful for specifying.

For each flight, the facility includes a drop down menu for selection of one or more media vendors. For each media type, the facility essentially comprises a set of media vendor partners (MVPs) as the supply side of the "marketplace" of the facility.

The screenshot in FIG. 26 shows as an example how an internet display ad can be purchased from Google AdSense or DoubleClick.

As an example, the facility includes APIs for vendors such as Google, Yahoo or MSN, for the purpose of purchasing and deploying standard “interfaces” and online display advertising and / or paid search.

The facility includes APIs for associating and performing digital purchases and digital placements of media consuming "orders" by the type of media.

To this end, the facility uses a multi-step process. The steps are as follows.

1. Firstly, the user interface presented by the facility has a button in its own architectural framework to launch the selected target "supply or sales side" (eg Google AdWords in the Internet search media category).

2. Next, the facility is a medium for end-users to "pipe-in" their unique usernames / passwords in order to initiate interaction with the seller-side platform (in this case, the Google AdWords Buying Portal). Has a parametrically-driven method.

3. Next, the facility batch-plays the buyer's time-phased flighting information, as if it were a pre-recorded data script through the user interface of the "supply or sales" platform. Pipe directly to that platform.

4. Finally, the facility allows the media buyer to pay for the purchased resources in a secure manner, thus completing the commercial transaction.

The facility uses these APIs to interact directly with the media source itself or through a third party such as media buying agencies or resellers.

3) Application of facilities for multichannel / multiplatform resources and / or media channels

The facility includes variations and applications for a range of users. These include the following:

● Multichannel Retailers

● Non-profit company

● Opening box office for movie

● Pricing Optimization and Dynamic Pricing

● New product or service

● small business

● advertising agency

● Customer lifetime values, including acquisition of new customers and retention of existing customers

● Optimize multiproduct and multigeography / market portfolios

Multiplatform media providers

● Trade channel funds, including market development funds

● Optimization of sales department size, mix, reach, frequency and location

● Optimize the location or branch of your store or office

● Investment and expenditure for product innovation

For example, a version for a multiplatform media provider may include both the main class and specific media types / vehicles proposed by the included media provider (s). Expand and apply the list. For example, a single media provider can provide multiple media types, such as media providers that can provide billboards, newspapers, and radio advertisements. In addition, a single media provider may be in a position to sell advertisements for a plurality of properties it controls, such as newspaper distribution, which owns newspapers in eight different cities. Examples of such suppliers are ESPN, MTV, L.A. Contains Times and Disney properties. For such providers, in some embodiments, the facility has been allocated cursively at the media provider level for individual properties and / or media types within the provider. The facility uses the same ACE calculations for this.

Those skilled in the art will appreciate that the aforementioned facilities can be easily adapted or extended in various ways.

Claims (21)

Overall marketing of the differentiated offerings for the purpose of optimizing the differentiated sales results for the offering that the computing system is expected to be driven by the allocation of resources to the overall marketing budget, at least in part. A computer readable medium having content for performing a method for automatically assigning resources to a budget, the method comprising:
The method comprises:
Receiving qualitative attributes of the distinguished property from a user;
Retrieving an empirically obtained average total marketing budget elasticity measure;
Obtaining additional data relating to the resilience to the distinguished property from a third party data source;
Adjusting the empirically obtained average overall marketing budget elasticity measure based on at least two of the received qualitative attributes of the distinguished property; And
Using the adjusted empirically obtained average total marketing budget elasticity measure along with the obtained related data to determine allocation of resources to an overall marketing budget that tends to optimize the differentiated sales results.
Computer-readable media comprising a. The computer-readable medium of claim 1, wherein the method for automatically assigning allocations of resources to an overall marketing budget for the distincted offering further comprises storing the determined allocations of resources. 10. The computer readable medium of claim 1, wherein the method for automatically assigning allocations of resources to an overall marketing budget for the identified offering further comprises displaying the determined allocations of resources to a user. In a computer system, at least in part, the optimization of resources for each of the activities for the purpose of optimizing the business outcome for the distincted listing is expected to be driven by one or more activities to be performed on the distinct listing. In a method for automatically assigning an assignment,
Receiving information characterizing the distinguished property of the user from a user;
For each of the activities, derived from empirical results for one or more listings that are distinguished from the distinguished listing but are determined to be similar to the distinguished listing based on the received information that characterizes the distinguished listing's attributes. Determining a resilience measure to be generated, wherein the resilience measure indicates an expected effect of the activity on the business result and the determining is performed based at least in part on information obtained from a third party information provider; And
Using the retrieved elasticity measures to generate an allocation of resources for each of the activities
How to include. The method of claim 4, wherein
The determining step,
Using the received information characterizing the first portion of the distinguished properties of the property, selecting an elasticity measure corresponding to the empirical results for the items for which the first portion of the properties is characterized in a similar manner; And
Adjusting the selected elasticity measure based on using received information characterizing a second portion of the distinguished properties of the property.
How to include. 5. The method of claim 4, further comprising, for at least one of the activities, automatically committing resources according to the allocation created for those activities. 5. The method of claim 4, further comprising displaying the allocation of the created resources to a user. 8. The method of claim 7, further comprising receiving a user input specifying an amount of media resources of a media type in response to displaying the allocation of the created resources to the user. The method of claim 8 further comprising presenting to the user a visual indication of at least one third party provider of the media resources of the media type. 10. The method of claim 9, further comprising receiving user input to select one of the indicated third party providers of the media resources of the media type. 11. The method of claim 10, further comprising placing an order for the amount of the media resource of the media type specified by the received user input to the selected third party provider of the media resource of the media type. 11. The method of claim 10, wherein the order for the amount of media resources is made automatically. In one or more computer memories that collectively store a generalized marketing elasticity data structure,
Multiple entries for each different sales profile—each sales profile describes a group of one or more sales listings that are qualitatively distinguished from a group of sales listings of different sales listing profiles, each entry being a group of sales listings Includes an elasticity measure that indicates the effect of marketing activities on sales results; And
Information obtained from third party data providers
Including,
For the distinctive sales for sale described by the distinct profile of the profiles, the elasticity measure represented by the distinct entry is obtained in order to automatically specify the allocation of marketing resources for the distinctive sales sale. One or more computer memories that can be used with the information. 15. The one or more computer memories of claim 13 further comprising storing the assignment of the designated resources. In a computing system, a method for automatically obtaining a final set of resource allocations that specify a quantitative allocation of resources for each of a plurality of marketing activities performed for a subject offering,
Accessing a first set of resource allocations for the subject offerings established using the first approach;
Accessing a set of quantitative lift factors for each of the plurality of marketing activities used in the first approach to establish a first one of the resource allocations;
Accessing a second set of resource allocations for the subject offerings established using a second approach distinct from the first approach; And
Using the set of accessed quantitative lift factors, combining the first set of accessed resource allocations and the second set of accessed resource allocations to obtain a final set of resource allocations for the target property.
How to include. 16. The method of claim 15, further comprising storing the final set of resource allocations. 16. The method of claim 15, further comprising displaying the final set of resource allocations to a user. A computer readable medium having content capable of causing a computing system to perform a method for ordering predetermined media resources for marketing the object offering for an offerer of the object,
The method, for each of a plurality of media types,
Causing a visual indication of an automatically recommended amount of media resources of the media type for the order to be presented to the user;
Receiving a user input specifying an actual amount of media resources of the media type for an order;
Causing visual indications of at least one third party provider of media resources of the media type to be presented to the user;
Receiving user input to select one of the indicated third party providers of media resources of the media type; And
Placing an order for the actual amount of the media resource of the media type specified by the received user input to the selected third party provider of the media resource of the media type.
Computer-readable media comprising a. The method of claim 18,
The method comprises:
For at least one of the plurality of media types,
Causing visual information to be presented to the user and requesting scheduling information for the media type; And
Receiving a user input specifying schedule information for the media type
Further comprising:
And the order placed comprises schedule information for the media type specified by the received user input. The method of claim 7, wherein
At least one of the placed orders includes payment information that enables the third party provider from whom the order was placed to obtain payment for the order from the provider. In a computing system, a method for automatically recommending resource allocations for marketing activities performed for a sale,
Determining resource allocation across the plurality of first level marketing activities using a set of quantitative lift factors for each of the plurality of first level marketing activities;
Associating one of the first level marketing activities with a non-zero resource allocation with the media resource provider; And
Determining resource allocation across the plurality of second level marketing activities using a set of quantitative lift factors for each of the plurality of second level marketing activities associated with the media resource provider.
How to include.

Images